work AI Deployment for Knowledge Workers

Every seat in your
organization
produces more.

Agntic deploys domain-specific AI agents trained on your company's knowledge. Not a chatbot. Not a generic AI tool. A bespoke system that knows your business, works alongside your team, and compounds in value the longer it runs.

Request a Discovery Call See How It Works
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handshake
back_hand
Agent Access
A capable agent working alongside every person on your team
article article article psychology
Your Knowledge
Every document, policy, and decision your organization has made
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Human Approval
Every agent action surfaces as a proposal. You decide before anything changes
person person person person
person person person person
× 2.4 per seat
Multiplied Output
Every seat in your organization produces measurably more
Purpose-built vs. general purpose
Agntic Copilot Claude Gemini
lockData privacy On-premises Cloud Cloud Cloud
constructionBespoke build Custom-built Off-the-shelf DIY Off-the-shelf
paletteBrand identity Your brand Microsoft Anthropic Google
securityData security Zero external exposure Shared infra Shared infra Shared infra
manage_accountsOngoing service Fully managed Self-service Requires eng. Self-service

How It Works.

A three-phase engagement. Discovery sets the baseline. Build deploys the system. Retainer holds us accountable to it — every quarter, by the numbers.

folder_open Docs & SOPs
→
workspaces Workflows
→
query_stats Metrics
Baseline output per seat: established ✓
Phase 01

Discovery

We begin with a structured audit of your team's most valuable workflows. Where does time disappear? Where do people re-find the same information repeatedly? Which outputs could be produced faster with the right answer already surfaced?

The output is a knowledge map and a measured baseline — output per seat, before the agent. That number becomes the benchmark every quarterly retainer review is scored against.

description SOPs
table_chart Data
picture_as_pdf Docs
Vault indexed
laptop_mac macOS
computer Windows
Phase 02

Build & Deploy

Your documents, SOPs, historical decisions, and data are ingested into a searchable vault — indexed for both semantic meaning and exact keyword matching simultaneously. The agent finds the right answer whether someone describes a concept or types a specific policy number or client name.

Delivered as a white-labeled native desktop app under your brand. No browser tab. No SaaS login screen. No third-party name visible to your staff. The app that lives in their dock says your firm.

autorenew
Vault
Model
Scoring
Tools
4.25
Q1 Score
Phase 03

Retainer

We manage the system on retainer. As your business changes, the vault changes with it. As better models become available, the agent is upgraded. New tools added, new workflows covered. The system compounds in proportion to how seriously your team uses it.

Every quarter we score the deployment across four dimensions — Adoption, Output, Vault Quality, and Reliability — and review the number with you. We walk into every retainer review with the score before you ask for it.

The agent proposes.
You decide.

AI that acts without permission is a liability, not an asset. Every output Agntic produces is a proposal — reviewed, approved, and owned by a human. That is not a feature. That is the operating principle.

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No autonomous actions. Ever.

The agent does not write, send, submit, or modify anything without an explicit human decision. Every action requires a click. Autonomy without oversight is not intelligence — it is risk.

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Accountability by design.

When something is approved, a person approved it. When something is rejected, a person rejected it. There is a clear line of ownership at every step — one that holds up under audit, compliance review, or a difficult client conversation.

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Trust earned, not assumed.

Trust in AI systems is built one approved proposal at a time. We do not ask your team to trust the agent on day one. We build that trust through consistent, transparent behavior — every output visible, every decision logged.

1 Proposed Change
Agent

Contract Summary · Section 4.2

The review timeline for standard agreements is

approximately 5–7 business days
typically 1–2 business days

when structured document intelligence is applied at intake.

Sourced from: contracts-sla-2024.pdf
database The Vault

Your knowledge base.
Always on. Always current.

The agent doesn't search the internet to answer your questions. It searches your organization. Every document, SOP, case file, and data export — indexed, fused, and retrieved on every turn.

Two search engines.
One answer.

Generic AI retrieval uses one path: semantic similarity. That works for concepts. It fails for exact terms — client IDs, policy numbers, contract clauses, product SKUs. Agntic runs both paths simultaneously and fuses them with Reciprocal Rank Fusion. Concept queries and exact-match queries both land correctly, every time.

psychology
Dense vector search

Understands meaning and context. Finds the right document even when your team doesn't use the exact words it contains.

search
BM25 keyword search

Finds exact terms. Policy codes, client names, product IDs — when precision matters more than interpretation, keyword search delivers.

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RRF fusion + MMR reranking

Both result sets are ranked and merged mathematically. Duplicate-penalized, diversity-weighted. The best answer floats to the top with no manual tuning.

Live Retrieval · vault_search
→
query: "limitation of liability Whitmore"
psychology Dense vector → 8 candidates sim 0.87
search BM25 keyword → 8 candidates exact
↓
RRF fusion · MMR rerank · top 4 returned
✓
Whitmore_Engagement_v4.docx · §8.4 relevance 0.94
Grade: relevant ✓  ·  corrective retry: not needed  ·  sources injected into context
sync

Live vault. Real-time ingest.

Drop a file into the vault folder and it is indexed within seconds — no manual uploads, no batch jobs. The agent has access to your latest documents the moment they land.

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Every format. Fully readable.

PDF, Word, Excel, PowerPoint, images, and scanned documents — all ingested via OCR and deep document parsing. Your knowledge base does not care what format your files are in.

fact_check

Grounded. Not hallucinated.

Every answer is grounded in retrieved source material. If the retrieval grade is low, the agent rewrites the query and retries. It does not guess when it can look it up.

Two deployment paths.
One cost curve that matters.

Cloud starts at zero and scales with every seat and query. Local is a one-time build investment. Around month 17, the lines cross — and the local advantage compounds from there.

Cloud / API-Based
Local Deployment
Upfront cost check_circleLow — pay as you go, no build cost One-time build investment. No recurring token or seat fees.
Token & inference costs Billed per token, per query — grows with every request and every seat check_circleZero — model runs locally, unlimited queries
Seat scaling cost Each new user adds API volume — cost accelerates as the team grows check_circleFlat cost — 10 seats or 100, the bill doesn't move
Vendor dependency Subject to OpenAI / Anthropic pricing changes, rate limits, and outages check_circleModel lives with your deployment — zero third-party dependency
Data privacy Documents and queries transmitted to and processed on external servers check_circleData never leaves your environment — compliant by architecture

Cumulative cost over 36 months

Illustrative model — 15-seat team, moderate query volume, one additional seat added per month after month 6.

Illustrative figures only. Cloud model: $700/mo base cost growing ~$20/mo as seat usage and token volume scale. Local model: one-time build investment, flat forever.

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Data stays on-premises

No documents, queries, or embeddings transmitted to external servers. Compliant by architecture, not by policy document.

all_inclusive

Unlimited token usage

No per-token billing, no throttling, no usage caps. Your team runs as many queries as the work demands — no one is watching a cost meter.

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API independent

If OpenAI changes pricing or Anthropic goes down, your system keeps running. The model lives with your deployment, not on someone else's server.

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Flat cost, growing return

Usage grows, headcount grows — the local cost line doesn't move. The more your team runs it, the lower the cost per output.

White-Label Deployments

Employees never
know it's us.

Every deployment ships under the client's name, logo, and color scheme. The app in your team's dock says your firm — not Agntic. This is not cosmetic. It determines whether people open it.

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Custom app name, icon, and color palette matched to your brand identity

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Native macOS and Windows — no browser tab, no SaaS login, no third-party branding visible to staff

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Agent persona scoped to the domain — a legal deployment knows contracts, not logistics

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Each client's vault, model, and interface is fully isolated — no shared infrastructure

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Harlow & Partners
Legal Intelligence
Live
Legal Intelligence

The firm standard is 12 months of fees.

Clause 8.4 in Whitmore falls short at 6 months — a revision has been proposed.

Holloway v. Ashby [2024] supports the 12-month position.

description Firm_Standard_Clauses.pdf
description Whitmore_v3.docx
trending_up
Meridian Capital
Portfolio Intelligence
Live
Portfolio Intelligence

Growth Fund returned 4.2% in March, outperforming S&P 500 by 1.8%.

Top contributors: MSFT (+12.3%), NVDA (+8.6%). Allocation within mandate.

Performance report drafted and ready for client distribution.

table_chart March_Performance.xlsx
description Portfolio_Mandate.pdf
apartment
Crest Property Group
Listings Intelligence
Live
Listings Intelligence

4 properties exceed 60 days: 14 Harborne Rd (72d), 7 Mill Lane (81d).

22 Poplar St (68d) and Flat 3B Kingsway (91d) also flagged for review.

Price adjustment recommended for 7 Mill Lane and Flat 3B Kingsway.

table_chart Thornton_Listings.xlsx
description Pricing_Guidelines.pdf
local_shipping
Vantage Operations
Operations Intelligence
Live
Operations Intelligence

Tier 1 SLA compliance: 94.2% for Q1. Two incidents exceeded the 4-hour window.

Both fell in the Northern region — root cause documented and escalated.

Corrective protocol initiated. Escalation report attached for review.

table_chart SLA_Tracker_Q1.xlsx
description Incident_Log_March.pdf
Powered by Agntic OS

The measure of every deployment is simple.

Does each seat produce more in less time than they did before?
If yes, the deployment is working. If not, we fix it.

Baseline output per seat
+ Agent access
+ Your organization's knowledge
+ Retrieval on every turn
+ Human-approved document drafting
Multiplied output per seat
Deployment health

Every query is graded. Every loop.

Faithfulness checks, retrieval quality scoring, and output gates run on every interaction. Degradation surfaces in hours — not quarters.

Faithfulness 91%
Answer grounded in retrieved vault sources
Retrieval quality 87%
Relevant documents retrieved per query
Output gate pass 96%
Structured outputs validated before delivery
Tool reliability 99%
Tool calls completing without error
93%
Deployment Health
Thriving
7-day rolling avg
across all queries

Three phases.
One continuous engagement.

Tailored engagements for teams that measure what AI actually does for their business.

One-time fee

Discovery

Map the workflow. Define use cases. Establish the output baseline your retainer will be scored against.

  • check_circle Workflow Audit
  • check_circle Use Case Definition
  • check_circle Output Baseline Measurement
Book Discovery
Project cost

Custom Build

Design, implement, and deploy the white-labeled desktop agent trained on your knowledge base. Native macOS and Windows.

  • check_circle Native macOS & Windows App
  • check_circle Custom Agent & Brand Identity
  • check_circle Knowledge Vault Injection
Schedule Build
Monthly recurring

Retainer

Vault maintenance. Model upgrades. New tools. Quarterly scoring. Continuous performance management.

  • check_circle Vault Maintenance
  • check_circle Quarterly Performance Scoring
  • check_circle Continuous Tool Library Upgrades
Request Access

Pricing discussed on a per-engagement basis. Every deployment is different in scope and team size.

Ready to measure what
AI actually does for your team?

Start with a Discovery call. We scope the workflow, define the use cases, and tell you exactly what output per seat should look like after 30 days.

Book a Discovery Call